Free Webinar
Revenue Growth Management Myths Unwrapped
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When “pricing is the biggest lever” becomes an excuse for shallow decisions
Re-examining the Foundations of Pricing Decisions
Most CPG organizations agree that pricing matters more than almost any other commercial lever. Yet that belief often collapses into simplified rules of thumb that feel directionally right but fail under real-world complexity.
This session challenges some of the most widely accepted assumptions in Revenue Growth Management and examines how they quietly distort decision quality. Rather than arguing against pricing or RGM, it exposes where commonly held beliefs break down once cost volatility, shopper heterogeneity, portfolio effects, and execution risk are taken seriously.
What You'll Learn
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The “1% price equals 6% EBITDA” claim only holds if you assume away demand response.
Once elasticity and execution risk are introduced, pricing advantage becomes conditional, not automatic. -
Cost-based pricing is not a regression from value, but a prerequisite for profit optimisation in CPG.
When variable costs are meaningful and volatile, ignoring them leads to structurally wrong pricing decisions. -
Value-based pricing breaks when organisations optimise to an average shopper that does not exist.
Real pricing power sits in understanding the full distribution of willingness to pay and the churn it creates. -
Aggressive price differentiation creates more downside than upside without near-perfect prediction accuracy.
Small errors in willingness-to-pay estimates erase gains faster than textbook models suggest. -
Price elasticity is a context-dependent outcome, not a fixed attribute of a product.
Treating it as a single number masks portfolio effects, competitive reactions, and price-level dynamics.
Watch this session if you are responsible for pricing decisions that must hold up under cost volatility, portfolio interactions, and real shopper behaviour.
Meet The Speaker
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Ingo ReinhardtCo-founder and Managing Director at Buynomics |
Before Buynomics, Ingo was a Senior Director with Simon-Kucher & Partners, a global leader in pricing. He holds a Ph.D. in Management from the University of Cologne and Master's degrees in Management and Mathematics. Ingo was a PostDoc at the University of Oxford and published in the Strategic Management Journal.
Session Highlights
The most cited pricing ROI chart assumes away reality
The popular EBITDA impact comparison rests on an implicit assumption of zero price elasticity, making pricing appear universally dominant by construction rather than evidence. [05:21]
Costs do not disappear just because we call pricing “value-based”
In CPG, meaningful variable costs directly shape profit-optimal prices, and ignoring them leads to systematically wrong decisions under volatility. [10:03]
Customer value is not a point estimate, it is a distribution
Optimizing to an “average shopper” hides churn risk and overstates pricing headroom when prices move. [14:22]
Perfect price differentiation is theoretically attractive and practically dangerous
Unless willingness-to-pay can be predicted with extreme precision, aggressive differentiation destroys profit rather than creating it. [19:27]
Elasticity is contextual, not a property of a SKU
The same product can exhibit radically different elasticities depending on price level, portfolio moves, and competitive actions. [21:20]
Survey-based thresholds exaggerate behavioural effects
What looks like a hard price threshold in research often softens dramatically in real shelf environments. [24:04]
Q&A
How should RGM teams balance cost increases with value-based pricing when willingness to pay lags behind costs?
Cost and demand must be reconciled through the demand curve, not intuition. If willingness to pay does not move, the curve stays fixed and determines how much cost can be passed through profitably.
If elasticity changes so much by context, how should teams actually use it?
Single elasticity numbers can guide rough intuition, but high-value decisions require modelling context explicitly. Precision matters most when differentiation or large moves are on the table.
How much historical data is needed to estimate elasticity credibly?
It depends on price variation. In stable-price environments, longer histories are required, while markets with frequent changes can yield insight from just a few years.
Where does AI genuinely add value in RGM today?
AI adds the most value in predicting how demand reacts to offer changes across portfolios. Peripheral use cases like interfaces or summaries are helpful but not core to RGM decision quality.
Will AI-driven price alerts on the consumer side change CPG pricing models?
These tools are still limited and currently more relevant online than offline. Their long-term impact remains uncertain and should not yet drive structural pricing changes.